A state-based regression formulation for domains with sensing actions and incomplete information

Le Chi Tuan, Chitta Baral, Tran Cao Son

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

We present a state-based regression function for planning domains where an agent does not have complete information and may have sensing actions. We consider binary domains and employ a three-valued characterization of domains with sensing actions to define the regression function. We prove the soundness and completeness of our regression formulation with respect to the definition of progression. More specifically, we show that (i) a plan obtained through regression for a planning problem is indeed a progression solution of that planning problem, and that (ii) for each plan found through progression, using regression one obtains that plan or an equivalent one.

Original languageEnglish (US)
Article number2
JournalLogical Methods in Computer Science
Volume2
Issue number4
DOIs
StatePublished - Oct 1 2006

Keywords

  • Conditional planning
  • Reasoning about action and change
  • Regression
  • Sensing actions

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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